These studies are directed toward the use of digital signal processing and automated analysis involving the application of statistical based techniques of information theory and mathematically based engineering methods, together with knowledge of clinical relevance to improve the diagnostic accuracy and prognostic power of electrocardiography, the most widely-used diagnostoc tool in cardiology. In the past this methodology was applied to routine, resting ECGs in studies of the Framingham population. However, though a single routine ECG may be useful in revealing relatively stable conditions (e.g. hypertrophy) it is a statistically insignificant sample of such changes occuring over hours. Whereas, monitoring electrocardiograms are recorded for longer periods (hours to days) than the usual routine ECG (<30 seconds). Monitoring ECGs can be recorded in several contexts: i) the 24 hour ambulatory ECG (AECG), ii) laboratory testing using AECG recorders, and iii) ECG monitoring in the intensive care. Many changes in cardiac status can occur within the longer times. Whether AECG is used in a clinical or research context, the outcome is critically dependent upon the quality and completeness of the data. A particular objective of this research is to carry forward previous work in biosignal analysis with the goal of implementing as much automation as possible to enable and expedite the interpretation of the huge streams of AECG data. This research has been redirected to time and frequency domain analysis of AECG data to determine the pathophysiological mechanisms of syncope in children and young adults while the patient undergoes table-tilt manipulations with and without drugs.